“Data is Destiny:” The Importance of Data Quality in the Age of Artificial Intelligence

In all business industries and science disciplines, consistent and dependable decision-making is centered around the acquisition of the best possible data to use as an input for determining the next, best action to take. Gut feelings, instincts, and tradition might work for specific individuals or in specific instances, but it is not a scalable or consistent problem-solving approach. In fact, one could argue that individuals with great instincts are just people who can, consciously or subconsciously, assess substantial amounts of data at once and derive proper judgments based on their knowledge and experience. Data-driven decision-making (DDDM) is the foundation for the scientific method and is at the core of any longstanding, successful enterprise.

There was a time where data collection was onerous and assessing the data was manual. This meant that organizations had to strike a critical balance between the speed and quality of their decision-making. We’ve always made decisions based on data; the process was slow, labor intensive, and required focus on a limited set of variables. There can be no denying that technology has changed the speed and scale of DDDM. With the capabilities present today in elastic cloud computing, big data, AI, and the Internet of Things, our ability to collect, process, and analyze data to make decisions has become exponentially faster and more wide-reaching. This automatically means better outcomes, right?

Consider the words of Joy Buolamwini, the MIT student who uncovered algorithmic bias in facial recognition programs and is a central figure in the Netflix documentary, Coded Bias. Buolamwini says, “If you are thinking about data and artificial intelligence, in many ways data is destiny. It is what we are using to teach machines different kinds of patterns. If you have largely skewed datasets that are being used to train these systems, you can also have skewed results.”  She said this in the context of racial bias in facial recognition programs, but the exact same principle can be applied to any AI model in a business or industrial setting.

Figure 1 - Garbage data plus a great model equals a garbage result.

Intelligent is not the default state of an AI model. AI models need to be taught how to think and they are taught by the data they are fed. An AI algorithm literally knows nothing else of the world but the 0s and 1s it is provided. In this context, you see the immense importance that is placed on data quantity and quality. As an organization digitally transforms its operations, data most certainly is destiny. Decisions in a digital world will always be faster, but whether they are better has a direct dependence on the quality of the input data.

Let me be clear, I do not say these things to diminish the promise of digital transformation or AI. On the contrary, I believe that organizations that do not embrace digital transformation will eventually become extinct. Slow never wins the race unless ALL other participants make crucial mistakes. I do, however, think it is critical to highlight the importance of addressing data quality as a foundational element to digital transformation.

Any digital transformation program should be built on a foundation of, at least, the following:

  1. Data standards optimized for data interoperability
  2. Data governance to ensure adherence to standards
  3. Strategic IT infrastructure roadmap with data interoperability as a primary objective
Figure 2 - The foundation of successful digital transformation programs are data standards, data governance, and infrastructure.
Elastic cloud computing, big data, IoT, and AI are vehicles with the potential to increase significantly the velocity of your organization’s data-driven decision-making. Fuel those vehicles with clean data and you will be heading in the right direction at tremendous speeds. Fuel it with bad data and you will be speeding towards a breakdown, or even worse, a fatal crash.
Scott Yates

Scott is an experienced technology consultant and a leader in facilities management, asset management and business operations. As Woolpert’s director of digital transformation, his focus will be on improving the data flow throughout the entire life cycle of facilities and systems.